Four design insights for GAI chatbots supporting SRL: real-time personalization, warm language, platform integration, and course-content embedding¶

Strategy · All strategies
Evidence · no claims cited

Description¶

From the three-year DBR process, the authors distill four insights for designing intelligent chatbots: enable real-time, context-sensitive personalization; use warm, conversational language supporting diverse pedagogical practices; integrate seamlessly into students' existing platforms and workflows; and embed course content and assessment links so the chatbot functions as a learning companion. They argue these design choices determine whether AI tools foster self-reflection or are perceived as generic or burdensome, and pair them with cultivating learners' dialogic competence through explicit instruction in prompting, goal setting, and metacognitive questioning.

Design Implications¶

Context¶

Requirements¶

• Educator involvement in designing, facilitating, and evaluating AI-mediated reflection aligned with pedagogical goals

Constraints¶

• Tools requiring unfamiliar platforms or disrupting habitual workflows risk abandonment; effectiveness is contingent on thoughtful integration rather than inherent

Target Learners¶

• higher education students

Target Learning Goals¶

• self-regulated learning
• reflective dialogue
• independent thought

Affordances¶

• Three Phase Srl Chatbot Scaffolding Framework

Related Strategies¶

Examples¶

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Key Sources¶

• Hilliger, I., Pérez-Sanagustín, M., González, C., Villalobos, E. and Celis, S. (2026). A GAI-Based Chatbot for Scaffolding Self-Regulated Learning: Insights from a Design-Based Research Approach. Journal of Learning Analytics, 13(1), 74–88. https://doi.org/10.18608/jla.2026.9143